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Complete Guide to Data Migration Testing. Data Migration Testing requires a comprehensive strategy in order to reduce risk and deliver a successful migration for the end users. In this article, David Katzoff, Managing Director of Valiance Partners, a specialist data migration technology and service provider, outlines a blueprint for designing an effective data migration testing strategy. David rounds off the article with a useful checklist of recommendations that the reader can use to benchmark their own approach. How to Implement an Effective Data Migration Testing Strategy. Compliance and business risk plays a significant role in the implementation of corporate information systems.

The risks associated with these systems are, in general, well known. However, as part of the implementation process, many of these information systems will be populated with legacy data and the compliance and business risks associated with data and content migrations are not necessarily understood. In this context, risks associated with data migration are a direct result of migration error. Further, industry testing strategies to mitigate such risk, or more specifically data migration error, lack consistency and are far from deterministic.

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This article offers thoughts and recommendations on how to create a more robust and consistent data migration testing strategy. Before diving into the detail, a bit of background - Valiance Partners has tested hundreds of data and content migrations, primarily in FDA regulated industries (pharmaceuticals, medical devices, bio- technologies and food products), manufacturing and autos. The information presented here includes some of the lessons learned from our clients’ quality control and the actual error history from testing the migrations of hundreds of thousands of fields and terabytes of content.

The recommended approach to designing migration testing strategies is to document one’s risk, the likelihood of occurrence, and then define the means to mitigate risk via testing where appropriate. The identification of risks is tricky and much of the process will be specific to the system being migrated. Let’s review two systems to illustrate this point: In the first case, migrating financial data in retail banking is typically defined by high volume migrations (1.

For a second example, consider a consumer products company’s complaints management. These systems are typically not mature and newer implementations, and their associated business processes, have to adapt to varying business and compliance requirements. These systems have modest volume in comparison (1. In both cases, getting the migrated data accurately represented in the destination system is critical.

However, the process by which accuracy is defined will vary significantly between these two systems and their associated migrations. In the first case, the financial services industry has evolved to the point where data interchange standards exist, which simplifies this process greatly. In the case where complaint management data is migrated, significantly more upfront analysis will be required to “best fit” the legacy data in the new system. This analysis will derive data enrichment to fill in incomplete records, identify data cleansing requirements through pre- migration analysis, dry runs the migration process and verify the results sting before understanding the final data migration requirements. System specific characteristics aside, there are several options for minimizing the occurrence of migration error through testing. The following discussion reviews these options and presents a set of recommendations for consideration.

Data Migration Testing: What are the options? The de facto approach to testing data and content migrations relies upon sampling, where some subset of random data or content is selected and inspected to ensure the migration was completed “as designed”. Those that have tested migrations using this approach are familiar with the typical iterative test, debug and retest method, where subsequent executions of the testing process reveal different error conditions as new samples are reviewed. Sampling works, but is reliant upon an acceptable level of error and an assumption pertaining to repeatability.

An acceptable level of error implies that less than 1. ANSI/ASQ Z1. 4). As per the assumption on repeatability, the fact that many migrations require four, five or more iterations of testing with differing results implies that one of the key tenets of sampling is not upheld, i. The following lists options for testing by the phase of the migration process: Pre- Data Migration Testing.

These tests occur early in the migration process, before any migration, even migration for testing purposes, is completed. The pre- migration testing options include: Verify scope of source systems and data with user community and IT. Verification should include data to be included as well as excluded and, if applicable, tied to the specific queries being used for the migration. Define the source to target high- level mappings for each category of data or content and verify that the desired type has been defined in the destination system. Verify destination system data requirements such as the field names, field type, mandatory fields, valid value lists and other field- level validation checks. Using the source to destination mappings, test the source data against the requirements of the destination system. For example, if the destination system has a mandatory field, ensure that the appropriate source is not null, or if the destination system field has a list of valid values, test to ensure that the appropriate source fields contain these valid values.

Test the fields that uniquely link source and target records and ensure that there is a definitive mapping between the record sets. Wildtangent Free Wild Coins. Test source and target system connections from the migration platform. Test tool configuration against the migration specification which can often be completed via black box testing on a field –by- field basis. If clever, testing here can also be used to verify that a migration specification’s mappings are complete and accurate. Formal Data Migration Design Review. Conduct a formal design review of the migration specification when the pre- migration testing in near complete, or during the earliest stages of the migration tool configuration. The specification should include: A definition of the source systems.

The source system’s data sets and queries. The mappings between the source system fields and the destination system. Number of source records.

Number of source systems records created per unit time (to be used to define the migration timing and downtime. Identification of supplementary sources. Data cleansing requirements. Performance requirements. Testing requirements. The formal design review should include representatives from the appropriate user communities, IT and management.

The outcome of a formal design review should include a list of open issues, the means to close each issue and approve the migration specification and a process to maintain the specification in sync with the migration tool configuration (which seems to continuously change until the production migration). Post- Data Migration Testing. Once a migration has been executed, additional end to end testing can be executed. Expect a significant sum of errors to be identified during the initial test runs although it will be minimized if sufficient pre- migration testing is well executed.

Post- migration is typically performed in a test environment and includes: Test the throughput of the migration process (number of records per unit time). This testing will be used to verify that the planned downtime is sufficient. For planning purposes, consider the time to verify that the migration process was completed successfully.

Compare Migrated Records to Records Generated by the Destination System – Ensure that migrated records are complete and of the appropriate context.